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recon_pet.py
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from sirf import STIR
from sirf import Reg
import argparse
from ast import literal_eval
import numpy as np
from sirf.Utilities import show_2D_array
from util import BinningConfig
from util import ListmodeData
from helper import get_image_resolution, get_image_voxel_size
from config import recon_parameters
def main(args):
##########
# binning
##########
listmode_data = ListmodeData(args.listmode)
binning_config = BinningConfig(
start=args.start_time, end=args.end_time, count_threshold=args.count_threshold
)
sinogram_data = listmode_data.to_sinogram(
args.sinogram_template, recon_parameters.sino_pre, binning_config
)
###################
# estimate randoms
###################
randoms_data = listmode_data.estimate_randoms()
################
# initial image
################
image_default = sinogram_data.create_uniform_image(
1.0, literal_eval(recon_parameters.resolution)
)
###################################
# set image resolution and spacing
###################################
if recon_parameters.resolution or recon_parameters.voxel_size:
image = STIR.ImageData()
image.initialise(
get_image_resolution(image_default, recon_parameters.resolution),
get_image_voxel_size(image_default, recon_parameters.voxel_size),
)
###########################
# define acquisition model
###########################
acqusition_model = STIR.AcquisitionModelUsingRayTracingMatrix()
acqusition_model.set_num_tangential_LORs(recon_parameters.num_LORs)
#######################
# read attenuation map
#######################
attn_image = STIR.ImageData(args.umap)
###############################################################
# create acquisition sensitivity model from normalisation data
###############################################################
asm_norm = STIR.AcquisitionSensitivityModel(args.norm_file)
#############################
# create attenuation factors
#############################
asm_attn = STIR.AcquisitionSensitivityModel(attn_image, acqusition_model)
asm_attn.set_up(sinogram_data)
bin_eff = sinogram_data.get_uniform_copy(value=1)
asm_attn.unnormalise(bin_eff)
asm_attn = STIR.AcquisitionSensitivityModel(bin_eff)
#####################
# scatter estimation
#####################
scatter_estimator = STIR.ScatterEstimator()
scatter_estimator.set_input(sinogram_data)
scatter_estimator.set_attenuation_image(attn_image)
scatter_estimator.set_randoms(randoms_data)
scatter_estimator.set_asm(asm_norm)
acf_factors = sinogram_data.get_uniform_copy()
acf_factors.fill(1 / bin_eff.as_array())
scatter_estimator.set_attenuation_correction_factors(acf_factors)
scatter_estimator.set_output_prefix(recon_parameters.sino_pre + "_scatter")
scatter_estimator.set_num_iterations(3)
scatter_estimator.set_up()
scatter_estimator.process()
scatter_estimate = scatter_estimator.get_output()
#########################################
# chain up attenuation and normalization
#########################################
asm = STIR.AcquisitionSensitivityModel(asm_norm, asm_attn)
acqusition_model.set_acquisition_sensitivity(asm)
acqusition_model.set_background_term(randoms_data + scatter_estimate)
#################
# reconstruction
#################
obj_fun = STIR.make_Poisson_loglikelihood(sinogram_data)
obj_fun.set_acquisition_model(acqusition_model)
recon = STIR.OSMAPOSLReconstructor()
recon.set_objective_function(obj_fun)
recon.set_num_subsets(recon_parameters.num_subsets)
recon.set_num_subiterations(recon_parameters.num_subiterations)
recon.set_up(image)
recon.set_current_estimate(image)
print("reconstructing...")
recon.process()
out = recon.get_output()
#####################
# Gaussian smoothing
#####################
smoothed = out.copy()
gaussian_filter = STIR.SeparableGaussianImageFilter()
gaussian_filter.set_fwhms(recon_parameters.fwhm)
gaussian_filter.set_max_kernel_sizes(recon_parameters.max_kernel_size)
gaussian_filter.set_normalise()
gaussian_filter.set_up(smoothed)
gaussian_filter.apply(smoothed)
if args.output_prefix:
Reg.NiftiImageData(smoothed).write(args.output_prefix)
print("reconstructed images saved.")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--listmode", type=str, help="listmode file")
parser.add_argument(
"--sinogram_template",
type=str,
help="the generated sinogram template file",
)
parser.add_argument("--norm_file", type=str, help="normalization file")
parser.add_argument("--umap", type=str, help="The attenuation umap file")
parser.add_argument(
"--start_time",
type=float,
default=0,
help="start time of time window for resconstruction",
)
parser.add_argument(
"--end_time",
type=float,
default=float("inf"),
help="start time of time window for resconstruction",
)
parser.add_argument(
"--output_prefix", type=str, default="output", help="the output filename prefix"
)
parser.add_argument(
"--count_threshold",
type=int,
default=20,
help="the count threshold to identify the real start time",
)
args = parser.parse_args()
main(args)